{"id":"https://openalex.org/W3045800504","doi":"https://doi.org/10.23919/acc45564.2020.9147832","title":"Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation","display_name":"Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3045800504","doi":"https://doi.org/10.23919/acc45564.2020.9147832","mag":"3045800504"},"language":"en","primary_location":{"id":"doi:10.23919/acc45564.2020.9147832","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc45564.2020.9147832","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 American Control Conference (ACC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080876002","display_name":"Alice Cortinovis","orcid":"https://orcid.org/0000-0001-6917-5106"},"institutions":[{"id":"https://openalex.org/I4210158120","display_name":"Swiss Epilepsy Center","ror":"https://ror.org/05xnnea38","country_code":"CH","type":"other","lineage":["https://openalex.org/I4210158120"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Alice Cortinovis","raw_affiliation_strings":["EPF Lausanne, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPF Lausanne, Switzerland","institution_ids":["https://openalex.org/I4210158120","https://openalex.org/I5124864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084601962","display_name":"Daniel Kre\u00dfner","orcid":"https://orcid.org/0000-0003-3369-2958"},"institutions":[{"id":"https://openalex.org/I4210158120","display_name":"Swiss Epilepsy Center","ror":"https://ror.org/05xnnea38","country_code":"CH","type":"other","lineage":["https://openalex.org/I4210158120"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Daniel Kressner","raw_affiliation_strings":["EPF Lausanne, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPF Lausanne, Switzerland","institution_ids":["https://openalex.org/I4210158120","https://openalex.org/I5124864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030708699","display_name":"Stefano Massei","orcid":"https://orcid.org/0000-0003-1813-4181"},"institutions":[{"id":"https://openalex.org/I4210158120","display_name":"Swiss Epilepsy Center","ror":"https://ror.org/05xnnea38","country_code":"CH","type":"other","lineage":["https://openalex.org/I4210158120"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Stefano Massei","raw_affiliation_strings":["EPF Lausanne, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPF Lausanne, Switzerland","institution_ids":["https://openalex.org/I4210158120","https://openalex.org/I5124864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027402421","display_name":"Benjamin Peherstorfer","orcid":"https://orcid.org/0000-0002-1558-6775"},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Peherstorfer","raw_affiliation_strings":["Courant Institute of Mathematical Sciences, New York University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Courant Institute of Mathematical Sciences, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2472","last_page":"2477"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12560","display_name":"Nuclear Engineering Thermal-Hydraulics","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.7507996559143066},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.7416513562202454},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7390013933181763},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.6693911552429199},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6684441566467285},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5118028521537781},{"id":"https://openalex.org/keywords/online-model","display_name":"Online model","score":0.49994730949401855},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.499467134475708},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4731384813785553},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.46800199151039124},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.44679829478263855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3929392993450165},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3856322169303894},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34215670824050903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30699920654296875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16440165042877197},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13916611671447754},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.0917733907699585}],"concepts":[{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.7507996559143066},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.7416513562202454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390013933181763},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.6693911552429199},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6684441566467285},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5118028521537781},{"id":"https://openalex.org/C2777851325","wikidata":"https://www.wikidata.org/wiki/Q7094102","display_name":"Online model","level":2,"score":0.49994730949401855},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.499467134475708},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4731384813785553},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46800199151039124},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.44679829478263855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3929392993450165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3856322169303894},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34215670824050903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30699920654296875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16440165042877197},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13916611671447754},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.0917733907699585},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/acc45564.2020.9147832","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc45564.2020.9147832","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 American Control Conference (ACC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/1136978","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1136978","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W49128507","https://openalex.org/W1162707989","https://openalex.org/W1934412359","https://openalex.org/W1966992674","https://openalex.org/W1973136150","https://openalex.org/W2047591100","https://openalex.org/W2049753327","https://openalex.org/W2052690618","https://openalex.org/W2084183309","https://openalex.org/W2088872157","https://openalex.org/W2133642820","https://openalex.org/W2144954107","https://openalex.org/W2152896489","https://openalex.org/W2254437089","https://openalex.org/W2768460243","https://openalex.org/W3104037750","https://openalex.org/W3121471846","https://openalex.org/W3122774201","https://openalex.org/W4289143476","https://openalex.org/W6756618176"],"related_works":["https://openalex.org/W2038693912","https://openalex.org/W1991602789","https://openalex.org/W1582396021","https://openalex.org/W1988359706","https://openalex.org/W312558119","https://openalex.org/W4210985407","https://openalex.org/W2335441444","https://openalex.org/W138014004","https://openalex.org/W2075598034","https://openalex.org/W2984441180"],"abstract_inverted_index":{"Traditional":[0],"model":[1,65],"reduction":[2,66],"derives":[3],"reduced":[4,20,37,56,77],"models":[5,21,38,57,78],"from":[6,97],"large-scale":[7,106,127],"systems":[8],"in":[9,22,79,90],"a":[10,98,147],"one-time":[11],"computationally":[12],"expensive":[13],"offline":[14,60,92],"(training)":[15],"phase":[16,25,82,93],"and":[17],"then":[18],"evaluates":[19],"an":[23,114],"online":[24,69,81],"to":[26,42,83,129,131,146,163],"rapidly":[27],"predict":[28,44],"system":[29,48,84],"outputs;":[30],"however,":[31],"this":[32,111],"offline/online":[33],"splitting":[34],"means":[35],"that":[36,50,75,86,138],"can":[39],"be":[40],"expected":[41],"faithfully":[43],"outputs":[45],"only":[46],"for":[47,121],"behavior":[49,85],"has":[51],"been":[52],"incorporated":[53],"into":[54],"the":[55,59,68,80,91,102,105,117,126,139,153,158],"during":[58],"phase.":[61],"This":[62],"work":[63,112],"considers":[64],"with":[67],"adaptive":[70],"empirical":[71],"interpolation":[72],"method":[73],"(AADEIM)":[74],"adapts":[76],"was":[87],"not":[88],"anticipated":[89],"by":[94,156],"deriving":[95],"updates":[96],"few":[99],"samples":[100],"of":[101,104,110,116,125],"states":[103,128],"systems.":[107],"The":[108,135],"contribution":[109],"is":[113,143],"analysis":[115,136],"AADEIM":[118,140,160],"sampling":[119,141,161,165],"strategy":[120,142,162],"deciding":[122],"which":[123],"parts":[124],"sample":[130],"learn":[132],"reduced-model":[133],"updates.":[134],"shows":[137],"optimal":[144],"up":[145],"factor":[148],"2.":[149],"Numerical":[150],"results":[151,155],"demonstrate":[152],"theoretical":[154],"comparing":[157],"quasi-optimal":[159],"other":[164],"strategies":[166],"on":[167],"various":[168],"examples.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
